Advertisement

Neural Networks in Intelligent Data Analysis

  • Xiaohui Liu

Abstract

The Intelligent Data Analysis Group at Birkbeck College has been working with several medical and industrial institutions on the use of a variety of computationally intelligent techniques to analyse large quantities of real-world data. Significant results have been obtained, and neural networks have played important roles in many of the interesting developments. In this chapter, aspects of data cleaning, data preprocessing and knowledge discovery will be discussed, and contributions from neural networks to these aspects will be described in the context of practical problem-solving environments. Moreover, we will demonstrate how neural networks can be effectively integrated with other methods to implement competent problem-solvers.

Keywords

Neural Network Input Pattern Output Neuron Test Location Mass Spectral Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Liu, X., “Intelligent data analysis: issues and challenges,” The Knowledge Engineering Review 11, 365–371 (1996).CrossRefGoogle Scholar
  2. [2]
    Wu, J. X., Visual Screening for Blinding Diseases in the Community Using Computer Controlled Video Perimetry, PhD thesis, University of London (1993).Google Scholar
  3. [3]
    Kohonen, T., Self-Organization and Associative Memory. Springer-Verlag (1989).Google Scholar
  4. [4]
    Kohonen, T., Self-Organizing Maps. Springer-Verlag (1995).Google Scholar
  5. [5]
    Obermayer, K. H., Ritter, H., and Schulten, K., “Large-scale simulations of self-organising neural networks on parallel computers: application to biological modeling,” Parallel Computing 14, 381–404 (1990).CrossRefGoogle Scholar
  6. [6]
    Schweizer, L. G., Parladori, G. L., Sicuranza, G. L., and Marsi, S., “A fully neural approach to image compression,” in Artificial Neural Networks, ed. T. Kohonen, K. Makisara, O. Simula, and J. Kandas, 815–820 (1991).Google Scholar
  7. [7]
    Favata, F. and Walker, R., “A study of the application of Kohonentype neural networks to the travelling salesman problem,” Biological Cybernetics 64, 463–68 (1991).zbMATHCrossRefGoogle Scholar
  8. [8]
    Kohonen, T., Makisara, K., and Saramaki, T., “Phonotopic maps—insightful representation of phonological features for speech recognition,” Proc. 7th Int. Conf. on Pattern Recognition (1984).Google Scholar
  9. [9]
    Ritter, H. J. and Kohonen, T., “Self-organizing semantic maps,” Biological Cybernetics 61, 241–254 (1989).CrossRefGoogle Scholar
  10. [10]
    Werbos, P. J., Beyond regression: new tools for prediction and analysis in the behavioral sciences, PhD Dissertation, Harvard University (1974).Google Scholar
  11. [11]
    Rumelhart, D.E. and McClelland, J. L., Parallel Distributed Processing, The MIT Press (1986).Google Scholar
  12. [12]
    Hecht-Nielsen, R., Neurocomputing, Addison-Wesley (1990).Google Scholar
  13. [13]
    Liu, X., Cheng, G., and Wu, J., “Managing the noisy glaucomatous test data by self-organizing maps,” In Proc. ICNN’94,IEEE International Conference on Neural Networks, 649–652, Orlando, Florida. IEEE Neural Networks Council (1994).Google Scholar
  14. [14]
    Kohonen, T., “The self-organising map,” Proc. IEEE 78:9, 1464–1480 (1990).CrossRefGoogle Scholar
  15. [15]
    Bauer, H. U. and Pawelzik, K. R., “Quantifying the neighborhood preservation of self-organizing feature maps,” IEEE Trans. on Neural Networks 3:4, 570–579 (1992).CrossRefGoogle Scholar
  16. [16]
    Liu, X., Cheng, G., and Wu, J. X., “Identifying the measurement noise in glaucomatous testing: an artificial neural network approach,” Artificial Intelligence in Medicine 6, 401–416 (1994).CrossRefGoogle Scholar
  17. [17]
    Grubbs, F. E., “Sample criteria for testing outlying observations,” Ann. Math. Statist. 21, 27–58 (1950).MathSciNetzbMATHCrossRefGoogle Scholar
  18. [18]
    Hawkins, A. D. M., “The detection of errors in multivariate data using principal components,” J. Amer. Statist. Assn. 69, 340–344 (1974).zbMATHCrossRefGoogle Scholar
  19. [19]
    Huber, P. J., Robust Statistics, John Wiley & Sons (1981).Google Scholar
  20. [20]
    Guyon, I., Matic, N., and Vapnik, V., “Discovering informative patterns and data cleaning,” Proc. AAAI’94 Workshop on Knowledge Discovery in Databases, 143–56 (1994).Google Scholar
  21. [21]
    Barnet, V. and Lewis, T., Outliers in Statistical Data, John Wiley & Sons (1994).Google Scholar
  22. [22]
    Liu, X., Cheng, G., and Wu, J., “Noise and uncertainty management in intelligent data modeling,” Proc. AAAI’94,12th National Conference on Artificial Intelligence, 263–268 (1994).Google Scholar
  23. [23]
    Wu, J., Cheng, G., and Liu, X., “Reasoning about outliers in visual field data,” submitted to IDA97 (1997).Google Scholar
  24. [24]
    Cheng, G., Liu, X., and Wu, J., “Interactive knowledge discovery through self-organising feature maps,” Proc. WCNN’94, World Congress on Neural Networks 4, 430–434, Orlando, Florida. IEEE Neural Networks Council (1994).Google Scholar
  25. [25]
    Frawley, W.J., Piatetsky-Shapiro, G., and Matheus, C. J., “Knowledge discovery in databases: an overview,” In Piatetsky-Shapiro, G. and Frawley, W. J., ed., Knowledge Discovery in Databases, 1–27. AAAI Press/The MIT Press (1991).Google Scholar
  26. [26]
    Smith, S., Bergeron, D., and Grinstein, G., “Stereophonic and surface sound generation for exploratory data analysis,” Proc. Conf of the Special Interest Group in Computer and Human Interaction (SIGCHI), 125–31 (1990).Google Scholar
  27. [27]
    Cheng, G., Liu, X., Wu, J., Jones, B., and Hitchings, R., “Discovering knowledge from visual field data: results in optic nerve diseases,” Proc. Medical Informatics Europe’96, ed. J. Brender et al., 629–633. IOS Press (1996).Google Scholar
  28. [28]
    Wu, J. X., Jones, B.R., Cassels-Brown, A., Adeniyi, F., and Abiose, A., “Topographical correlation between motion sensitivity test locations and onchocercal chorioretinal lesions,” Acta XXVIII Int. Cong. Ophthal. (1994).Google Scholar
  29. [29]
    Cheng, G., Liu, X., Wu, J. X., and Jones, B., “Establishing a reliable visual function test and applying it to screening optic nerve disease in onchocercal communities,” International Journal of Bio-Medical Computing 41, 47–53 (1996).CrossRefGoogle Scholar
  30. [30]
    Johnson, R. G., Liu, X., Phalp, M., Dettmar, H., and Payne, A., “Advanced data pro-processing in scams,” Proc. 4th European Congress on Intelligent Techniques and Soft Computing, 1595–1599 (1996).Google Scholar
  31. [31]
    Payne, A., Phalp, J. M., and Windig, W., “A modified simplisma approach for the resolution of mixtures using data from automated probe mass spectrometry,” Analytica Chimica Acta 318, 43–53 (1995).CrossRefGoogle Scholar
  32. [32]
    Dettmar, H. J., Johnson, R. G., Liu, X., Mannock, K., Newson, P., Phalp, M., and Payne, A., “An integrated approach to chemical structure characterisation using knowledge and data,” Proc. 13th Annual Expert Systems Conference, ed. I. Graham, 203–18 (1993).Google Scholar
  33. [33]
    Dettmar, H. J., Johnson, R. G., Liu, X., Phalp, M., and Payne, A., “The acquisition and application of meta-knowledge in the scams system,” AI and Cognitive Science 94, ed. M. Keane, P. Cunningham, M. Brady, and R. Byrne, 75–88 (1994).Google Scholar
  34. [34]
    Wright, L., Wilcox, R., Wu, J. X., Fitxke, F., Wormald, R., and Johnson, G., “Motion sensitivity testing in occupational health screening,” Perimetry Update 1994/95, 335–338 (1994).Google Scholar
  35. [35]
    Rogers, S.K., Kabrisky, M., Ruck, D., and Oxley, M., Neural networks for fighting crime, 406–15 (1994).Google Scholar
  36. [36]
    Martin del Brio, B. and Serrano-Cinca, C., “Self-organising neural networks for the analysis and representation of data: some financial cases,” Neural Computing & Applications 1, 193–206 (1993).CrossRefGoogle Scholar
  37. [37]
    Ghahramani, Z. and Jordan, M.I., “Supervised learning from incomplete data via an em approach,” In Advances in Neural Information Processing Systems, ed. J. D. Cowan et al., Vol. 6,120–127. Morgan Kaufmann (1996).Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Xiaohui Liu
    • 1
  1. 1.Dept of Computer Science Birkbeck CollegeUniversity of LondonLondonUK

Personalised recommendations